(1) Field of the Invention
This invention relates to a method of image-processing a radiographic image with a subject falling thereon, and radiographic apparatus using thereof. More particularly, this invention relates to an image-processing method that allows noise reduction processing, high frequency enhancement processing, and dynamic range compression processing, and radiographic apparatus using the method.
(2) Description of the Related Art
Medical institutions are equipped with radiographic apparatus for acquiring an image of a subject with radiation. When an image is subject to given image processing, the image has a noise removed therefrom or a structure of such as a blood vessel emphasized that falls thereon, which may result in easier diagnosis. Accordingly, the conventional radiographic apparatus may process an acquired image through image processing. Specifically, examples of the image processing in which the radiation photography apparatus may adopt include noise reduction processing, high frequency enhancement processing, and dynamic range compression processing. See Japanese Patent Publications No. H10-171983, H10-75364, H10-75395, H9-163227, H10-63837, and H10-63838.
In order to perform the above three image processing, two or more band images need to be generated from a radiographic image appearing a subject (hereinafter, appropriately referred to as a source image.) Here, the band image is an image formed of only frequency components in a certain band in the source image. A given frequency component is extracted from the source image through application of a band-pass filter for passing the frequency components of the band in the source image. Two or more band images are generated based on the source image. They differ from one another in band with the frequency components extracted from the source image. Consequently, one band image contains only high frequency components in the source image, whereas another band image contains only low frequency components in the source image. The band images are sequentially generated from a high frequency component side through image processing to the source image. Here, the high frequency component in the source image is a component with a detailed structure in a projected image of the subject falling thereon. The low frequency component in the source image is a component with a rough structure in the projected image of the subject falling thereon.
Now, description will be given of one method of generating a conventional band image. A first method uses a high-pass filter H and a band-pass filter B as illustrated in FIG. 18. According to the first method, the high-pass filter H is applied to the source image P0, as illustrated in FIG. 18(a). The high-pass filter H is an image filter specified by a matrix that extracts the high frequency components from the source image P0. FIG. 18 schematically illustrates the high-pass filter H applied to an upper left end of the source image P0, whereby high frequency components are extracted in the upper left end of the source image P0. This operation is shown by arrows in dotted lines. The high-pass filter H is applied to the source image P0 while moving with respect thereto, and the high frequency component is extracted throughout the source image P0, whereby a first band image α is generated having only the high frequency components falling thereon. The first band image α has the same size as the source image P0. In FIG. 18, image conversion processing using the high-pass filter H is expressed with a symbol Hpf.
Next, a band-pass filter B is applied to the source image P0, as illustrated in FIG. 18(b). The band-pass filter B is an image filter having a larger specified matrix than the high-pass filter H. FIG. 18 schematically illustrates the band-pass filter B applied on an upper left end of the source image P0, and thus a component is extracted within a higher frequency range in the upper left end of the source image P0. The frequency component extracted at this time has lower frequencies than the component extracted through the high-pass filter H. Consequently, with the bypass filter H, the frequency component is extracted from the source image P0 that is lower than that in the band of the first band image α. This operation is shown by arrows in dotted lines. The band-pass filter B is applied to the source image P0 while moving with respect thereto, and the high frequency components are extracted throughout the source image P0, whereby a second band image β is generated having only the components in the higher frequency range falling thereon. The second band image β has the same size as the source image P0. In FIG. 18, image conversion processing using the band-pass filter B is expressed by a symbol Bpf.
Thereafter, the source image P0 is reduced in size for generating a reduction image P1 (see FIG. 18(c).) The same operation as that in FIG. 18(b) is performed to the reduction image P1 for generating a third band image γ. Subsequently, the reduction image P1 is also reduced in size for generating a reduction image P2 (see FIG. 18(d).) The same operation as that in FIG. 18(b) is performed to the reduction image P2 for generating a fourth band image δ. In FIG. 18, the process for reducing an image is expressed by a symbol Mag(−).
In general, more components of the low frequencies in the source image are extracted as the matrix specifying the band-pass filter B increases in dimension with respect to the image. When the matrix specifying the band-pass filter B increases in dimension so as to extract the lower frequency components from the source image, parameters in the matrix increase, which leads to time-consuming for filtering. Here, the foregoing configuration reduces an image to be used for the image conversion processing instead of increasing in size the band-pass filter B upon extraction of the components of the low frequencies. Accordingly, it is not necessary to increase in dimension the matrix specifying the band-pass filter B, which results in high-speed image processing. As illustrated in FIG. 18, the images as a source of the band images β, δ, γ are small in this order. As a result, the band images β, δ, γ have components of the low frequency in this order in the source image P0. As noted above, the band images α, β, δ, γ are generated having the frequency components in various frequency bands extracted from the source image P0. The band images α, β, δ, γ have the extracted low frequency components in the source image P0 in this order, and are used for noise reduction processing, etc.
Description will be given of another method of generating a band image. A second method uses a low-pass filter L as illustrated in FIG. 19. According to the second method, the low-pass filter L is applied to the source image P0, as illustrated in FIG. 19. The low-pass filter L is an image filter specified by a matrix that may remove the high frequency components from the source image P0. FIG. 19 schematically illustrates the high-pass filter H applied on an upper left end of the source image P0, and removes the high frequency components in the upper left end of the source image P0. This operation is shown by arrows in dotted lines. The low-pass filter L is applied to the source image P0 while moving with respect thereto, and the high frequency components are removed throughout the source image P0, whereby a low-pass image L0 is generated having the high frequency components removed therefrom.
Next, description will be given of a method of generating the first band image α. In order to generate the first band image α, the low-pass image L0 is subtracted from the source image P0, as illustrated by a path in dashed lines in FIG. 19. Taking into consideration that the low-pass image L0 is an image having the high frequency components removed from the source image P0, the high frequency components contained in the source image P0 are outputted through the subtraction. This corresponds to the first band image α. The first band image α, the low-pass image L0, and the source image P0 all have the same size. In FIG. 19, image conversion processing using the low-pass filter L is expressed by a symbol Lpf.
Upon generation of the second band image β, the source image P0 is firstly reduced in size for generating the reduction image P1. The same operation as above is performed to the reduction image P1 for generating a low-pass image L1. The low-pass image L1 is magnified so as to have the same size as the low-pass image L0, whereby a magnified low-pass image M1 is generated. Thereafter, the magnified low-pass image M1 is subtracted from the low-pass image L0 as illustrated by a path in dashed lines in FIG. 19. The subtraction result is the second band image β. In FIG. 19, a process for reducing an image is expressed by a symbol Mag(−), and a process for magnifying an image by Mag(+).
Here, more components are removed having the lower frequencies than the source image as the matrix specifying the low-pass filter L increases in dimension with respect to the image. According to the second method, the matrix specifying the low-pass filter L increases in dimension with respect to the reduction image as the image with the low-pass filter L applied thereto is reduced. Reduction of the image may realize the same effect as that obtained through increasing in dimension of the matrix specifying the low-pass filter. In comparison of the low-pass image L0 and the magnified low-pass image M1, the magnified low-pass image M1 has more removed components of low frequencies.
The magnified low-pass image M1 is subtracted from the low-pass image L0, as illustrated by a path in dashed lines in FIG. 19, whereby the high frequency component in the low-pass image L0 is outputted. This corresponds to the second band image β. The second band image β, the low-pass image L0, and the magnified low-pass image M1 all have the same size.
Upon generation of the third band image γ, the reduction image P1 is firstly reduced in size for generating the reduction image P2. The same operation as above is performed to the reduction image P2 for generating a low-pass image L2. The low-pass image L2 is magnified so as to have the same size as the low-pass image L1, whereby a magnified low-pass image M2 is generated. Thereafter, the magnified low-pass image M2 is subtracted from the low-pass image L1, as shown by a path in dashed lines in FIG. 19, to acquire the third band image γ. The third band image γ, the low-pass image L1, and the magnified low-pass image M2 all have the same size. As noted above, the band images α, β, δ, γ are generated having the frequency components in various frequency bands extracted from the source image P0, and are used for noise reduction processing, etc.
However, the foregoing image processing method by the radiographic apparatus has following drawbacks.
According the first method, there arises a drawback of time-consuming generation of the band image. Upon generation of the band image, the low frequency components need to be extracted from the image. Consequently, filtering has to be performed using a matrix in a large dimension. The conversion process using the matrix with a large dimension needs increased pixel data for calculation, which leads to much time involved. Particularly, it takes most time for generating the second band image β. That is because the matrix in a larger dimension has to be applied throughout the source image P0 in a larger size. Such slow processing may be a problem particularly in real time processing to moving images.
In addition, according to the second method, there arises a problem that a false image may appear in the band image to be generated and an artifact may occur in the image generated through the noise reduction processing to the false image, etc. That is, the second method needs to have steps of further reducing the source image and further magnifying the low-pass image than in the first method for generating the band image. In general, when a reducing process is performed to generate a reduction image and a filter in an original size is applied to the reduction image instead of applying the magnified filter to an image, the low-pass image in the reduced image is to deteriorate than that in the source image. Moreover, further magnified processing may degrade the low-pass image in the reduced image than that in the source image.
The reason for the above will be described. The reduction image is an image obtained through bundling of pixels in the source image together for reduction. The reduction image corresponds to an image in which a box filter is applied to the source image for performing discrete sampling. Here, the number of pixels forming the reduction image is fewer than that of the source image. Consequently, even when the filter in the original size is applied to the reduced image, processing is not performed similarly to a case where the magnified filter is applied to the source image under an influence that the box filter is applied in advance. In addition, generation of the reduction image is irreversible image processing. Information on the source image having the defect reduction image cannot be recovered completely although it may be estimated through interpolation. Consequently, although the image is magnified, defect information due to the discrete sampling cannot be recovered completely, but image quality thereof may deteriorate. There is no other way not to reduce or magnify the image for preventing artifacts from occurring in the image. Accordingly, the low-pass filter L is to be applied to the source image P0 while the matrix increases in dimension. As a result, the band image with no artifact may be acquired, but on the other hand, it takes much time for generating the image.
This invention has been made regarding the state of the art noted above, and its object is to provide an image processing method that allows image processing at low calculation load while possibly suppressing artifacts occurring in the image processing, and radiographic apparatus using the method.